Přehled o publikaci
2024
Comparison of Large Language Models for Generating Contextually Relevant Questions
LODOVICO MOLINA, Ivo; Valdemar ŠVÁBENSKÝ; Tsubasa MINEMATSU; Li CHEN; Fumiya OKUBO et al.Basic information
Original name
Comparison of Large Language Models for Generating Contextually Relevant Questions
Authors
LODOVICO MOLINA, Ivo; Valdemar ŠVÁBENSKÝ; Tsubasa MINEMATSU; Li CHEN; Fumiya OKUBO and Atsushi SHIMADA
Edition
Proceedings of the 19th European Conference on Technology Enhanced Learning (ECTEL), 2024
Other information
Language
English
Type of outcome
Konferenční abstrakta
Confidentiality degree
is not subject to a state or trade secret
Marked to be transferred to RIV
No
Organization
Repository – Repository
ISBN
978-3-031-72312-4
Keywords in English
Generative AI; Question Generation; AI in Education
Changed: 16/9/2024 00:50, RNDr. Daniel Jakubík
Abstract
In the original language
This study explores the effectiveness of Large Language Models (LLMs) for Automatic Question Generation in educational settings. Three LLMs are compared in their ability to create questions from university slide text without fine-tuning. Questions were obtained in a two-step pipeline: first, answer phrases were extracted from slides using Llama 2-Chat 13B; then, the three models generated questions for each answer. To analyze whether the questions would be suitable in educational applications for students, a survey was conducted with 46 students who evaluated a total of 246 questions across five metrics: clarity, relevance, difficulty, slide relation, and question-answer alignment. Results indicate that GPT-3.5 and Llama 2-Chat 13B outperform Flan T5 XXL by a small margin, particularly in terms of clarity and question-answer alignment. GPT-3.5 especially excels at tailoring questions to match the input answers. The contribution of this research is the analysis of the capacity of LLMs for Automatic Question Generation in education.
